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In previous studies, decoding electroencephalography (EEG) signals has notconsidered the topological relationship of EEG electrodes. However, the latestneuroscience has suggested brain network connectivity. Thus, the exhibitedinteraction between EEG channels might not be appropriately measured viaEuclidean distance. To fill the gap, an attention-based graph residual network,a novel structure of Graph Convolutional Neural Network (GCN), was presented todetect human motor intents from raw EEG signals, where the topologicalstructure of EEG electrodes was built as a graph. Meanwhile, deep residuallearning with a full-attention architecture was introduced to address thedegradation problem concerning deeper networks in raw EEG motor imagery (MI)data. Individual variability, the critical and longstanding challengeunderlying EEG signals, has been successfully handled with the state-of-the-artperformance, 98.08% accuracy at the subject level, 94.28% for 20 subjects.Numerical results were promising that the implementation of thegraph-structured topology was superior to decode raw EEG data. The innovativedeep learning approach was expected to entail a universal method towards bothneuroscience research and real-world EEG-based practical applications, e.g.,seizure prediction.

Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals